Something-Something. Tab. REF  compares the recognition performance of gsf with state-of-the-art approaches on SS-V1 and SS-V2 datasets. For fair comparison, we compare only with those methods that use RGB frames for feature extraction. We also report the number of frames used by each approach along with the number of parameters and the computational complexity in terms of flop in the table. Since different approaches use different backbone cnn, we also report the backbones used for fair comparison. The table is split into two, the first part lists approaches that use 2D cnn or efficient implementations of 3D cnn as backbone. The second part shows the methods that rely on heavier 3D cnn for spatio-temporal feature extraction. Our baseline TSN [1]} obtains 17.52% and 32.71% recognition accuracy on SS-V1 and SS-V2 datasets, respectively. Applying gsf to the backbone cnn, an absolute gain of +31.79% and +30.2% is obtained on the two datasets. This huge improvement is obtained with an increase of only +0.48% and +0.55% on the number of parameters and flop. Compared to the top performing approaches that use 2D cnn, such as TEA [2]} and MSNet [3]}, gsf performs competitively. On the other hand, approaches that uses 3D cnn such as I3D [4]} and Non-local networks [5]}, considerably falls behind gsf. The approach from [6]} uses a deeper 3D cnn (I3D ResNet-152). When compared to the performance obtained with a backbone  cnn of equivalent depth (ResNet-50), gsf outperforms the approach from [6]} by +1.3%. By plugging in the MotionSqueeze module [3]} on the GSF version of ResNet-50, our model surpasses the recently developed state-of-the-art approaches MVF-Net [9]} and CT-Net [10]}, achieving an accuracy of 53.78% on SS-V1.
